Abstract
We propose a dynamic programming approach to address the problem of determining how a structured formation of autonomous undistinguishable agents can be reorganized into another, eventually non-rigid, formation based on changes in the environment, perhaps unforeseeable. The methodology can also be used to correctly position the agents into a particular formation from an initial set of random locations. Given the information of the current agents location and the final locations, there are n! possible permutations for the n agents, and we seek the one that minimizes a total relative measure, e.g. distance traveled by the agents during the switching. Possible applications can be found amongst surveillance, damage assessment, chemical or biological monitoring, among others. where the switching to another formation, not necessarily predefined, may be required due to changes in the environment.

Abstract
This paper describes a novel approach in formation control for mobile robots. Here, a Nonlinear Model Predictive Controller (NMPC) is used to maintain the formation of three omnidirectional mobile robots. The details of the controller structure are presented as well as its functionality in a soccer robot team. Three Middle Size League Robots are used for evaluation. A case of study based in a soccer robot situation is presented, developed, and implemented to evaluate the performance of the controller. Simulations results are presented and discussed.

Abstract
This paper describes a novel approach in formation control for mobile robots. A Nonlinear Model Predictive Controller (NMPC) is used to control the formation of a heterogeneous mobile robots group. The desired formation is formed by an holonomic robot and a nonholonomic robot. The same nonlinear controller is used in both robots with the same cost function. The details of the controller structure are presented in order to track a fixed target departing from different positions in the field avoiding collisions with each other. A soccer robot competition field is used to present the simulations to evaluate the performance of the controller. © 2012 Springer-Verlag Berlin Heidelberg.

Abstract
In this paper, we investigate normal and nondegenerate forms of the maximum principle for optimal control problems with state constraints. We propose new constraint qualifications guaranteeing nondegeneracy and normality that have to be checked on smaller sets of points of an optimal trajectory than those in known sufficient conditions. In fact, the constraint qualifications proposed impose the existence of an inward pointing velocity just on the instants of time for which the optimal trajectory has an outward pointing velocity.

Abstract
In this paper, we address the generation of electrical power using Airborne Wind Energy Systems, comprising a kite connected through a tether to a generator on the ground. We design a controller to steer the kite to follow a pre-defined periodic path, which includes a production mode, a tether retrieval mode, and a safe mode capable of handling wind gusts. Copyright

Abstract
In this work, we address through model predictive control (MPC) a constrained nonlinear plant described by a continuous-time dynamical model, which naturally leads to a sampled data control system. The numerical solution of the optimal control problems involved in MPC must utilize, eventually, some form of discretization. Nevertheless, there are several advantages in maintaining a continuous-time model until later stages. One advantage is that we can devise numerical procedures which, by exploiting additional freedom in selecting the discretization points, are more efficient when continuous-time models are used. Here, we discuss an extension to MPC of an Adaptive Mesh Refinement (AMR) algorithm, which has shown to be efficient in solving nonlinear optimal control problems. We derive a sufficient condition that guarantees that an MPC scheme using an adaptive time mesh refinement algorithm preserves stability.